An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review
Background Artificial intelligence (AI) has become a prominent technique for medical
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
Deep learning for segmentation of brain tumors: Impact of cross‐institutional training and testing
Background and purpose Convolutional neural networks (CNN s) are commonly used for
segmentation of brain tumors. In this work, we assess the effect of cross‐institutional training …
segmentation of brain tumors. In this work, we assess the effect of cross‐institutional training …
Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario
Purpose Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has
wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence …
wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence …
Automatic brain tumor segmentation with scale attention network
Y Yuan - Brainlesion: Glioma, Multiple Sclerosis, Stroke and …, 2021 - Springer
Automatic segmentation of brain tumors is an essential but challenging step for extracting
quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis …
quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis …
Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks
A cascade of fully convolutional neural networks is proposed to segment multi-modal
Magnetic Resonance (MR) images with brain tumor into background and three hierarchical …
Magnetic Resonance (MR) images with brain tumor into background and three hierarchical …
Automated brain tumour segmentation using cascaded 3d densely-connected u-net
M Ghaffari, A Sowmya, R Oliver - … , Stroke and Traumatic Brain Injuries: 6th …, 2021 - Springer
Accurate brain tumour segmentation is a crucial step towards improving disease diagnosis
and proper treatment planning. In this paper, we propose a deep-learning based method to …
and proper treatment planning. In this paper, we propose a deep-learning based method to …
Brain tumour image segmentation using deep networks
Automated segmentation of brain tumour from multimodal MR images is pivotal for the
analysis and monitoring of disease progression. As gliomas are malignant and …
analysis and monitoring of disease progression. As gliomas are malignant and …
Deep learning neural networks for medical image segmentation of brain tumours for diagnosis: a recent review and taxonomy
S Devunooru, A Alsadoon, PWC Chandana… - Journal of Ambient …, 2021 - Springer
Brain tumour identification with traditional magnetic resonance imaging (MRI) tends to be
time-consuming and in most cases, reading of the resulting images by human agents is …
time-consuming and in most cases, reading of the resulting images by human agents is …
Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools
Background Brain cancer is a destructive and life-threatening disease that imposes
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …
Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review
Brain is an amazing organ that controls all activities of a human. Any abnormality in the
shape of anatomical regions of the brain needs to be detected as early as possible to reduce …
shape of anatomical regions of the brain needs to be detected as early as possible to reduce …